Frequency-Enhanced Subspace Clustering Network With Information Bottleneck

In data mining, subspace clustering is a crucial technique which determines the union of the underlying subspace to cluster data points in an unsupervised manner. Although deep-learning-based subspace clustering, typically referred to as deep subspace clustering (DSC), has significantly improved clustering accuracy, existing DSC models still struggle to capture a comprehensive and compact latent representation as they generally explore the spatial domain to extract useful information and face difficulty in balancing the high mutual and low redundant information between the original input space and latent subspace. This leads to the performance of the model being dependent on initialization, resulting in a lack of stability. In this study, a novel network is proposed to extract features in both the frequency domain and spatial domain. We introduce three types of ResBlocks in the discrete Fourier transform (DFT), discrete cosine transform (DCT), or discrete wavelet transform (DWT) frequency domains separately to learn both the low-frequency and high-frequency information in the proposed networks. Additionally, to extract concise and rich latent representations, IB loss is employed by deriving a variational lower bound on the IB objective. Extensive experiments on several benchmark datasets verify the effectiveness of our networks compared to state-of-the-art models. In addition, detailed ablation studies are performed to demonstrate the advantages of the two introduced components.